A deep learning framework for sequence-based bacteria type IV secreted effectors prediction

Desvaux, 2009, Secretion and subcellular localiz.ations of bacterial proteins: a semantic awareness issue, Trends Microbiol., 17, 139, 10.1016/j.tim.2009.01.004 Costa, 2015, Secretion systems in Gram-negative bacteria: structural and mechanistic insights, Nat. Rev. Microbiol., 13, 343, 10.1038/nrmicro3456 Gerlach, 2007, Protein secretion systems and adhesins: the molecular armory of Gram-negative pathogens, Inter. J. Med. Microbiol.: IJMM, 297, 401, 10.1016/j.ijmm.2007.03.017 Cascales, 2003, The versatile bacterial type IV secretion systems, Nat. Rev. Microbiol., 1, 137, 10.1038/nrmicro753 Fronzes, 2009, The structural biology of type IV secretion systems, Nat. Rev. Microbiol., 7, 703, 10.1038/nrmicro2218 Alvarez-Martinez, 2009, Biological diversity of prokaryotic type IV secretion systems, Microbiol. Mol. Biol. Rev.: MMBR (Microbiol. Mol. Biol. Rev.), 73, 775, 10.1128/MMBR.00023-09 Juhas, 2008, Type IV secretion systems: tools of bacterial horizontal gene transfer and virulence, Cell Microbiol., 10, 2377, 10.1111/j.1462-5822.2008.01187.x Li, 1999, Essential components of the Ti plasmid trb system, a type IV macromolecular transporter, J. Bacteriol., 181, 5033, 10.1128/JB.181.16.5033-5041.1999 Christie, 2001, Type IV secretion: intercellular transfer of macromolecules by systems ancestrally related to conjugation machines, Mol. Microbiol., 40, 294, 10.1046/j.1365-2958.2001.02302.x Hofreuter, 2001, Natural transformation competence in Helicobacter pylori is mediated by the basic components of a type IV secretion system, Mol. Microbiol., 41, 379, 10.1046/j.1365-2958.2001.02502.x Ding, 2003, The outs and ins of bacterial type IV secretion substrates, Trends Microbiol., 11, 527, 10.1016/j.tim.2003.09.004 Ward, 2001, The six functions of Agrobacterium VirE2, Proc. Natl. Acad. Sci. U. S. A., 98, 385, 10.1073/pnas.98.2.385 Schroder, 2002, TraG-like proteins of DNA transfer systems and of the Helicobacter pylori type IV secretion system: inner membrane gate for exported substrates?, J. Bacteriol., 184, 2767, 10.1128/JB.184.10.2767-2779.2002 Hofreuter, 2000, Genetic competence in Helicobacter pylori: mechanisms and biological implications, Res. Microbiol., 151, 487, 10.1016/S0923-2508(00)00164-9 Zou, 2013, Accurate prediction of bacterial type IV secreted effectors using amino acid composition and PSSM profiles, Bioinformatics, 29, 3135, 10.1093/bioinformatics/btt554 Wang, 2014, Prediction of bacterial type IV secreted effectors by C-terminal features, BMC Genomics, 15, 50, 10.1186/1471-2164-15-50 An, 2018, Comprehensive assessment and performance improvement of effector protein predictors for bacterial secretion systems III, IV and VI, Briefings Bioinf., 19, 148 Wang, 2017, Effective prediction of bacterial type IV secreted effectors by combined features of both C-termini and N-termini, J. Comput. Aided Mol. Des., 31, 1029, 10.1007/s10822-017-0080-z Wang, 2017, Systematic analysis and prediction of type IV secreted effector proteins by machine learning approaches, Briefings Bioinf., 10.1093/bib/bbx164 An, 2017, SecretEPDB: a comprehensive web-based resource for secreted effector proteins of the bacterial types III, IV and VI secretion systems, Sci. Rep., 7, 41031, 10.1038/srep41031 Huang, 2010, A web server for clustering and comparing biological sequences, Bioinformatics, 26, 680, 10.1093/bioinformatics/btq003 UniProt, 2010, The universal protein resource (UniProt) in 2010, Nucleic Acids Res., 38, D142, 10.1093/nar/gkp846 Meyer, 2013, Searching algorithm for type IV secretion system effectors 1.0: a tool for predicting type IV effectors and exploring their genomic context, Nucleic Acids Res., 41, 9218, 10.1093/nar/gkt718 Makino, 2003, Genome sequence of Vibrio parahaemolyticus: a pathogenic mechanism distinct from that of V cholerae, Lancet, 361, 743, 10.1016/S0140-6736(03)12659-1 Vergunst, 2000, VirB/D4-dependent protein translocation from Agrobacterium into plant cells, Science, 290, 979, 10.1126/science.290.5493.979 Simone, 2001, The carboxy-terminus of VirE2 from Agrobacterium tumefaciens is required for its transport to host cells by the virB-encoded type IV transport system, Mol. Microbiol., 41, 1283, 10.1046/j.1365-2958.2001.02582.x Vergunst, 2005, Positive charge is an important feature of the C-terminal transport signal of the VirB/D4-translocated proteins of Agrobacterium, Proc. Natl. Acad. Sci. U. S. A., 102, 832, 10.1073/pnas.0406241102 Marchesini, 2011, In search of Brucella abortus type IV secretion substrates: screening and identification of four proteins translocated into host cells through VirB system, Cell Microbiol., 13, 1261, 10.1111/j.1462-5822.2011.01618.x Ke, 2015, Type IV secretion system of Brucella spp. and its effectors, Front. Cell. Infect. Microbiol., 5, 72, 10.3389/fcimb.2015.00072 Hubel, 1963, Shape and arrangement of columns in cat's striate cortex, J. Physiol., 165, 559, 10.1113/jphysiol.1963.sp007079 LeCun, 2015, Deep learning, Nature, 521, 436, 10.1038/nature14539 Angermueller, 2016, Deep learning for computational biology, Mol. Syst. Biol., 12, 878, 10.15252/msb.20156651 Alipanahi, 2015, Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning, Nat. Biotechnol., 33, 831, 10.1038/nbt.3300 Kelley, 2016, Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks, Genome Res., 26, 990, 10.1101/gr.200535.115 Zhou, 2015, Predicting effects of noncoding variants with deep learning-based sequence model, Nat. Methods, 12, 931, 10.1038/nmeth.3547 Kim, 2018, Deep learning improves prediction of CRISPR-Cpf1 guide RNA activity, Nat. Biotechnol., 36, 239, 10.1038/nbt.4061 Szalkai, 2018, SECLAF: a webserver and deep neural network design tool for hierarchical biological sequence classification, Bioinformatics, 34, 2487, 10.1093/bioinformatics/bty116 Almagro Armenteros, 2017, DeepLoc: prediction of protein subcellular localization using deep learning, Bioinformatics, 33, 3387, 10.1093/bioinformatics/btx431 Veltri, 2018, Deep learning improves antimicrobial peptide recognition, Bioinformatics, 34, 2740, 10.1093/bioinformatics/bty179